What is Propensity to Buy?
By definition, it is the likelihood of a potential customer to purchase something, a.k.a Purchase Propensity. It is applicable to almost any sort of business, however, it’s especially applicable to the e-commerce industry.
E-commerces experience very regular website traffic and every single visitor is a potential customer. In reality, not everyone ends up purchasing something. However, they all have one thing in common. They all have some website visit behavior and some likelihood in terms of the probability to purchase something.
A Propensity to Buy Model predicts which of these visitors (potential customers) are going to purchase something and which are not. Propensity to Buy uses results from a test mailing or previous campaign to generate scores. The scores indicate which contacts are most likely to respond. The Response field indicates who replied to the test mailing or previous campaign. The Propensity fields are the characteristics that you want to use to predict the probability that contacts with similar characteristics will respond.
What Makes it Important?
E-commerce websites attract traffic On average 2% of the visitors actually buy something. These visitors have certain behaviors that distinguish the ones that buy something and the ones that don’t. These visitors can be modeled using their website visit behavior to predict their likelihood to purchase something. This has two hidden benefits:
- The company can differentiate visitors that are highly likely to buy something along with their behaviors.
- They can target these highly likely visitors to take immediate marketing actions like; campaigns, coupons promotions, etc.
Reaching out to the right visitors who are higher likely to purchase will secure the potential sales and eventually in long run increase the revenue.
How to Predict Propensity to Buy?
There are of course more than one way to achieve this on many different platforms. Some are simpler than others and some are more accurate than others here in Catwing everything is simple and easy and its design is to simplify the process once we integrate all relevant data sources.
Example. The direct marketing division of a company uses results from a test mailing to assign propensity scores to the rest of their contact database, using various demographic characteristics to identify contacts most likely to respond and make a purchase.